1. Field of the Invention
The present invention relates to a method and system for analyzing medical image data, more particularly, detecting ischemic stroke by comparing texture attributes of image data.
2. Description of the Related Art
With aging of our population, stroke has been rated the third highest cause of death in the world. Ischemic stroke occurs as a result of blood clot or thrombus in an artery or the development of fatty deposits lining the vessel walls. A computer-aided detection (CAD) scheme can be served as an alert system for detection of abnormality that may easily be overlooked by clinicians or a tool to improve the accuracy of diseases detection. The use of mathematical models for computer-aided detection (CAD) has achieved certain success in the radiological science.
Strokes can be broadly divided into hemorrhagic and ischemic strokes from management point of view. A lacunar stroke is a subtype of ischemic stroke. It is a blockage of blood flow to apart of the brain supplied by one or more small arteries. This type of stroke is relatively difficult to identify, as it manifests as a small hypodense area of less than 15 mm in diameter.
Clinically, Computed Tomography (CT) remains the choice for evaluation of patients with suspected acute ischemic stroke because CT is more accessible, inexpensive, efficient and reliable method to obtain images of a body parts. Since an accurate imaging diagnosis is critical for a patient's survival, it is desirable to establish a computer-aided system for early detection of ischemic stroke.
Clinical diagnosis of lacunar stroke is difficult if the assessment is based on features appear within first few hours after the onset of stroke. Winbeck et al., “Transient Ischemic Attack and Stroke Can Be Differentiated by Analyzing Early Diffusion-Weighted Imaging Signal Intensity Changes”, describes that small lesion might be missed even on high resolution diffusion-weighted imaging (DWI). Therefore, early detection of lacunar stroke is crucial and this necessitates a more efficient method to improve the detection rate.
Previous attempts have been made for the detection of acute stroke such as normalization of the CT image into a standard atlas to identify hypodensity within the insula ribbon and lentiform nucleus (Talairach et al., “A Co-Planar Stereotaxic Atlas of the Human Brain: An Approach to Medical Cerebral Imaging”, Thieme); applying an adaptive partial smoothing filter (APSF) (Lee et al., “Detectability improvement of early sign of acute stroke on brain CT images using an adaptive partial smoothing filter”) to reduce noise component and enhance important signal components; using wavelet-based image processing to improve the contrast and de-noise the subtle signs of hypodensity of the image locally (Przelaskowski, et al., “Improved early stroke detection: wavelet-based perception enhancement of computerized tomography exams”).
Also, there was an attempt to use wavelet decomposition of the histogram and difference of energy measure to detect ischemic stroke (Chawla, et al., “A method for automatic detection and classification of stroke from brain CT images”). However, their sample size was still too small to substantiate a valid fully automatic method. Thus, a CAD scheme for automatic detection of ischemic stroke still needs further elaborations and improvements.